HANDOFF.md

# Handoff: Automatic Layer Selection for Hallucination Detection

Last updated: 2026-07-20 17:06 UTC

Paper: **Automatic Layer Selection for Hallucination Detection**  
arXiv: [2605.26366](https://huggingface.co/papers/2605.26366)  
OpenReview: [srAuLCB3Ca](https://openreview.net/forum?id=srAuLCB3Ca)

## Read this first

**An exact reproduction of the paper is virtually impossible from the released
artifacts.** The authors did not release the generated `best_answer` records,
the ten uncertainty candidates per input, judge decisions/labels, exact model
and dataset snapshots, dependency lock, selected layers, trained probe states,
or raw results. The released `method/ID_twonn.py` computes a TwoNN curve but
does not implement the paper's FEPoID peak selector. In addition, the paper says
"exact string match" while the released labeling code uses substring
containment. Any result produced here is therefore a pinned **independent,
scaled replication**, not a regeneration of the authors' exact AUROCs.

The current experimental scope is 1,000 flattened CoQA training examples and
1,000 flattened validation examples for Llama. The paper uses 10,000 training
examples, the full 7,983-example CoQA test set, and averages five QA datasets.
Do not describe the current run as verification of the paper's five-dataset
Table 2 mean.

## Persistent assets

- Llama A100 Job (active at handoff):
  <https://huggingface.co/jobs/GwendalTsang/6a5e54a9d09dc1f57c6bd8f8>
- Llama hidden-state/result bucket:
  <https://huggingface.co/buckets/GwendalTsang/fepoid-llama-hidden-states>
- Mistral TPU v5e-8 extractor repository:
  <https://github.com/GwenTsang/mistral-tpu8-hidden-states>
- Mistral one-core verification artifact:
  <https://huggingface.co/datasets/GwendalTsang/mistral-7b-hidden-states-tpu-verification>
- **Mistral TPU v5e-8 hidden-state bucket (add after the full repository run):**
  `<PASTE_HF_BUCKET_URL_HERE>`
- Canonical reproduction logbook target (not yet published):
  <https://huggingface.co/spaces/GwendalTsang/repro-automatic-layer-selection-for-hallucination-detection>
- Public handoff bundle (created after this file was written):
  <https://huggingface.co/datasets/GwendalTsang/automatic-layer-selection-reproduction-bundle>
- Public handoff snapshot bucket:
  <https://huggingface.co/buckets/GwendalTsang/automatic-layer-selection-reproduction-handoff>

## Current live Llama run

Job ID: `GwendalTsang/6a5e54a9d09dc1f57c6bd8f8`  
Job name: `fepoid-llama-coqa-2k-v3`  
Hardware: one A100 80 GB (`a100-large`, $2.50/hour)  
Hard timeout: 110 minutes (maximum substantive-run charge about $4.59)  
Docker image: `pytorch/pytorch:2.7.1-cuda12.8-cudnn9-runtime`

At the snapshot time, the job was `RUNNING`. Both Llama answer-generation
splits had completed: 1,000 train + 1,000 validation examples in 162.49 seconds.
The job was downloading/loading the pinned Ministral judge. It is remote and
will continue after the current TPU session is terminated.

Monitor it with:

```bash
hf jobs inspect GwendalTsang/6a5e54a9d09dc1f57c6bd8f8 --format json
hf jobs logs GwendalTsang/6a5e54a9d09dc1f57c6bd8f8 --tail 200
hf jobs wait GwendalTsang/6a5e54a9d09dc1f57c6bd8f8 --timeout 30s --format json
```

The first two launch attempts are useful negative provenance, not successful
experiments:

- `6a5e5423d09dc1f57c6bd8f0`: inline `bash -lc` was parsed incorrectly; exit 127
  before inference.
- `6a5e545dd09dc1f57c6bd8f4`: launcher worked but `scikit-dimension` had an
  undeclared Matplotlib import; failed before model loading. The pinned
  dependency was added before v3.

The Jobs canary succeeded:
<https://huggingface.co/jobs/GwendalTsang/6a5e5324d09dc1f57c6bd897>.

### Pinned inputs

- Model: `meta-llama/Llama-3.1-8B-Instruct`
- Model revision: `0e9e39f249a16976918f6564b8830bc894c89659`
- Judge: `mistralai/Ministral-8B-Instruct-2410`
- Judge revision: `2f494a194c5b980dfb9772cb92d26cbb671fce5a`
- Dataset: `stanfordnlp/coqa`
- Dataset revision: `0d9e9952f1ef6e5415492d3d84b5873259137e3c`
- Generation: seed 2024, temperature 0.1, 30-token cap
- FEPoID forward horizon: `w=7`
- The ten temperature-1 uncertainty candidates are intentionally omitted
  because FEPoID itself does not consume them.

### Expected persisted output

Bucket prefix: `coqa-2k-llama-20260720/`

- `environment.json`
- `prepared/*.jsonl`: generated answers, FST answers, labels, judge outputs,
  model/dataset revisions, and source indices
- `hidden_states/llama/{full,fst}/{train,validation}/manifest.json`
- 16 SafeTensors shards in total: four views/splits times four 250-record shards
- Every shard contains:
  - `hidden_states`: BF16 `[records, 32, 4096]`
  - `labels`: int64 `[records]`
  - `record_indices`: int64 `[records]`
- `metrics/llama_{full,fst}_layers.csv`
- `metrics/llama_{full,fst}_summary.json`
- `gpu_stage_summary.json`
- `SUCCESS`

Layer semantics match Transformers `output.hidden_states[1:]`: layers 0–30 are
post-block/pre-final-RMSNorm representations and layer 31 is post-final-block,
post-final-RMSNorm. Inputs are left padded; index `-1` is the last non-padding
token. `use_cache=False` is used for extraction. Full and FST answers are
retokenized using the paper release's prompt-plus-answer construction.

## Verify the Llama artifact after completion

Do not treat `COMPLETED` alone as proof. Check the bucket and the success marker:

```bash
hf buckets list GwendalTsang/fepoid-llama-hidden-states \
  --recursive --human-readable --format json

mkdir -p outputs/llama-coqa-2k
hf buckets sync \
  hf://buckets/GwendalTsang/fepoid-llama-hidden-states/coqa-2k-llama-20260720 \
  outputs/llama-coqa-2k

test -f outputs/llama-coqa-2k/SUCCESS
find outputs/llama-coqa-2k/hidden_states -name '*.safetensors' | wc -l
```

The shard count should be 16. Validate every manifest's record count, shape,
dtype, and SHA-256 against the downloaded shard. Also ensure every prepared
split has 1,000 rows and both labels are present in train and validation before
trusting AUROC. If the job fails after exporting some shards, do not mix partial
and rerun output under the same prefix; change the prefix in
`scripts/run_llama_hf_job.sh` and relaunch.

## Local workspace layout

- `/content/main.tex`, `/content/sections/`, `/content/tables/`: extracted paper
  source used for the audit.
- `/content/paper-code/`: authors' repository at commit `def3cb6`.
- `/content/repro_automatic-layer-selection-for-hallucination-detection/`:
  scaled independent reproduction bundle.
- `/content/.trackio/logbook/`: canonical five-claim Trackio logbook scaffold.
- `/content/mistral-tpu8-hidden-states/`: published eight-device TPU extractor,
  clean at commit `10cc151` on `main`.

Important reproduction files:

- `configs/scaled_coqa.json`: pinned revisions, scale, and limitations.
- `scripts/hybrid_gpu_stage.py`: Llama generation, judging, BF16 extraction,
  sharding, TwoNN/FEPoID, and MLP probes. It now supports `--models llama`
  without loading Mistral.
- `scripts/run_llama_hf_job.sh`: exact dependency lock and v3 Job command.
- `scripts/repro_common.py`: reconstructed FEPoID, FST scanner, TwoNN, probes.
- `scripts/analyze_mistral_tpu.py`: consumes provided TPU Mistral artifacts.

Local checks completed:

- Python compilation passed for all three Python scripts.
- `hybrid_gpu_stage.py --self-test` passed.
- A SafeTensors round trip confirmed BF16 `[N,32,4096]` export and sharding.
- The Mistral TPU repository has 5 passing unit tests and a successful GitHub
  Actions run:
  <https://github.com/GwenTsang/mistral-tpu8-hidden-states/actions/runs/29761410557>.
- A real one-core TPU v5e Mistral smoke test passed. Eight-device orchestration
  was not available in the original session, so the extractor enforces
  `--expected-world-size 8` to prevent silent underuse.

## Mistral handoff contract

The user explicitly said they will provide the Mistral hidden states later.
**Do not spend GPU/TPU money regenerating Mistral unless the user changes that
instruction.** Ask for or locate the provided Mistral output, then validate it.

Preferred Mistral shape is BF16 `hidden_states=[N,32,4096]` plus JSONL metadata
with stable record order and labels for full/FST views. The published extractor
also saves `embedding=[N,4096]`; the analysis does not require the embedding.
Run its validator first, then adapt/execute:

```bash
python /content/mistral-tpu8-hidden-states/validate_output.py /path/to/mistral-output
python scripts/analyze_mistral_tpu.py --help
```

Never compare Llama and Mistral AUROC unless they use the same flattened CoQA
indices, split definitions, prompt format, generated-answer policy, correctness
labeling policy, FST rule, probe split, and layer indexing.

## Remaining work, in order

1. Monitor v3 to a terminal state and perform the artifact audit above.
2. Download only small summaries/manifests first; record wall time, estimated
   cost, label balance, selected layers, full/FST AUROCs, last-layer AUROCs, and
   oracle AUROCs in the logbook.
3. Add a validator for all Llama manifests/shards if the current Job succeeds.
4. When the user supplies Mistral activations, validate and analyze them with
   the same split/probe conventions. Do not regenerate Mistral by default.
5. Populate Trackio claim pages:
   - Claim 1: Llama layer-wise AUROC curve and whether its maximum is
     intermediate.
   - Claim 2: scaled CoQA FEPoID versus last/max-ID/oracle; clearly state that
     five-dataset means 0.7253/0.8531 were not reproduced.
   - Claim 3: not empirically tested yet; a summarization run is still needed
     for substantive verification.
   - Claim 4: time the reconstructed selector separately from activation
     extraction and probes; the paper's 10.14-second cross-benchmark mean cannot
     be regenerated from author artifacts.
   - Claim 5: compare the same records under full versus FST and report paired
     AUROC changes.
6. Build the required Chenruishuo/posterly poster with `--strict-polish`, add
   `poster_embed.html` to Executive summary, and pin it below the summary.
7. Register the dedicated reproduction folder as the Conclusion artifact.
8. Validate and publish:

```bash
curl -sL https://huggingface.co/spaces/ICML-2026-agent-repro/challenge/raw/main/scripts/validate_icml_logbook.py | \
  python3 - --space GwendalTsang/repro-automatic-layer-selection-for-hallucination-detection

trackio logbook publish \
  GwendalTsang/repro-automatic-layer-selection-for-hallucination-detection
```

The logbook is only a scaffold at handoff; it has **not** been validated or
published. Its Executive summary must prominently repeat that exact
reproduction is virtually impossible and distinguish the scaled independent
replication from the paper's full experiment.